MALAT1: A regulator of inflammatory cytokines in diabetic complications
Bibliographic record
Abstract
OBJECTIVES AND DESIGN: In this study, we examined the role of MALAT1, a highly conserved nuclear long non-coding RNA molecule, in chronic diabetic complications affecting the heart and kidneys using both in vitro and in vivo models: human endothelial cell culture and a Malat1 knockout mice model. RESULTS: Findings from our in vitro experiments demonstrated that MALAT1 was predominantly localized to nuclear speckles in endothelial cells and MALAT1 expression was significantly increased following incubation with high glucose in association with increased expression of inflammatory cytokines. As for our in vivo experiments, we used Malat1 knockout mice and wild-type controls with or without streptozotocin-induced diabetes over 2 months of follow-up, where all of our diabetic animals showed hyperglycaemia and polyuria. Examination of cardiac and renal tissues demonstrated altered MALAT1 RNA expression in wild-type diabetic animals. Such changes were associated with augmented production of downstream inflammatory molecules at the mRNA and protein levels. Diabetes-induced elevations of inflammatory markers were significantly decreased in Malat1 knockout diabetic animals. In addition to transcript and protein analyses, we examined functional changes in the heart and kidneys. Organ functions were affected in the wild-type diabetic mice but were rescued in Malat1 knockout mice. CONCLUSIONS: Taken together, findings from this study will provide direct evidence and insight into the importance of MALAT1 in the pathogenesis of chronic diabetic complications involving the heart and kidneys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".